During the past 50 years, a large amount of theoretical and practical research has been devoted to image-processing techniques for enhancing, restoring, and extracting information from images. Many image-enhancement and image-restoration methods are carried out in the frequency domain, following a Fourier transform of an image from the spatial domain to the frequency domain. However, in many cases, frequency-domain-based image-enhancement and image-restoration operations produce periodic artifacts and distortions in a resulting restored image obtained by an inverse Fourier transform of an image processed in the frequency-domain back to the spatial domain. There are various types of information in the spatial domain that can be used to direct or constrain various image-enhancement and image-restoration operations in order to prevent introduction of periodic artifacts and distortions, but that information is generally either unavailable or difficult to apply in the frequency domain. For these reasons, theoreticians and researchers who work on image-processing-related problems, developers and vendors of image-processing systems, users of devices and systems that incorporate image-processing components, and consumers of processed images have all recognized the need for improved image-processing methods and systems that can effectively apply spatial-domain information to direct and constrain image-processing operations in order to produce high-quality restored images in as most efficient and cost-effective manner as possible.